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Generative Adversarial Networks (GANs) have shown success in approximatingcomplex distributions for synthetic image generation. However, currentGAN-based methods for generating biometric images, such as iris, have certainlimitations: (a) the synthetic images often closely resemble images in thetraining dataset; (b) the generated images lack diversity in terms of thenumber of unique identities represented in them; and (c) it is difficult togenerate multiple images pertaining to the same identity. To overcome theseissues, we propose iWarpGAN that disentangles identity and style in the contextof the iris modality by using two transformation pathways: IdentityTransformation Pathway to generate unique identities from the training set, andStyle Transformation Pathway to extract the style code from a reference imageand output an iris image using this style. By concatenating the transformedidentity code and reference style code, iWarpGAN generates iris images withboth inter- and intra-class variations. The efficacy of the proposed method ingenerating such iris DeepFakes is evaluated both qualitatively andquantitatively using ISO/IEC 29794-6 Standard Quality Metrics and the VeriEyeiris matcher. Further, the utility of the synthetically generated images isdemonstrated by improving the performance of deep learning based iris matchersthat augment synthetic data with real data during the training process.

iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images